Wekws: A Production First Small-Footprint End-to-End Keyword Spotting Toolkit
Jie Wang, Menglong Xu, Jingyong Hou, Binbin Zhang, Xiao-Lei Zhang, Lei Xie, Fuping Pan
Abstract
Keyword spotting (KWS) enables speech-based user interaction and gradually becomes an indispensable component of smart devices. Recently, end-to-end (E2E) methods have be-come the most popular approach for on-device KWS tasks. However, there is still a gap between the research and deployment of E2E KWS methods. In this paper, we introduce WeKws, a production-quality, easy-to-build, and convenient-to-be-applied E2E KWS toolkit. WeKws contains the implementations of several state-of-the-art backbone networks, making it achieve highly competitive results on three publicly available datasets. To make WeKws a pure E2E toolkit, we utilize a refined max-pooling loss to make the model learn the ending position of the keyword by itself, which significantly simplifies the training pipeline and makes WeKws very efficient to be applied in real-world scenarios. The toolkit is publicly available at https://github.com/wenet-e2e/wekws.
BibTeX
@inproceedings{icassp2023_wekwsaproduction,
title = {Wekws: A Production First Small-Footprint End-to-End Keyword Spotting Toolkit},
author = {Jie Wang and Menglong Xu and Jingyong Hou and Binbin Zhang and Xiao-Lei Zhang and Lei Xie and Fuping Pan},
booktitle = {ICASSP 2023},
year = {2023}
}